In this Letter, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov chain Monte Carlo (MCMC) methods are problematic. More specifically, we show that generative models can be used to estimate the absolute value of the free energy, which is in contrast to existing MCMC-based methods, which are limited to only estimate free energy differences. We demonstrate the effectiveness of the proposed method for two-dimensional ${\varphi }^4$ theory and compare it to MCMC-based methods in detailed numerical experiments.

中文翻译：

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用深度生成模型估计晶格场理论中的热力学观测值

在这封信中，我们证明了将深度生成机器学习模型应用于晶格场理论是解决马尔可夫链蒙特卡洛（MCMC）方法存在问题的有希望的途径。更具体地说，我们表明，可以使用生成模型来估计自由能的绝对值，这与现有的基于MCMC的方法（仅限于估计自由能差）相反。我们证明了所提出的二维方法的有效性${\varphi }^4$ 理论，并在详细的数值实验中将其与基于MCMC的方法进行比较。